Integrating Neural Language Models in Clinical Practice: A Case Study on Chronic Kidney Disease Consultations

Abstract

This study explores the integration of large language models (LLMs) into medical consultations, specifically in the care of chronic kidney disease (CKD) patients. Nephrology, the branch of medicine focused on kidney health, relies heavily on detailed patient histories and complex clinical reasoning. To support this process, we collected real-world consultation data in partnership with a hospital and developed an AI-driven framework to assist in transcription and clinical documentation. Our system automates speech-to-text conversion and structured text refinement, leveraging Whisper for transcription and GPT-4o for medical reasoning and summarization. The model achieved ROUGE-L F1 scores of 0.43 for patient phrases, 0.57 for medical team phrases, and 0.63 overall, with TF-IDF cosine similarity scores reaching 0.76, 0.89, and 0.91, respectively. Clinical outputs were evaluated by specialized LLMs, OpenBioLLM-70B and DeepSeek v3, with most predictions aligning with or providing plausible alternatives to physician assessments. These findings highlight the potential of LLMs to enhance clinical workflows by reducing documentation burdens while maintaining diagnostic accuracy.

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